Intelligent Lighting System and Control Method

Abstract

The present invention provides an intelligent lighting system and a control method. The intelligent lighting system includes a plurality of intelligent light poles, and each intelligent light pole is provided with a lighting unit, a processing unit, a communication unit, and at least one environmental monitoring sensor. The processing unit is configured to: first, obtain first environmental data of the current light pole and second environmental data of adjacent light poles of the current light pole; secondly, predict third environmental data of the current light pole in a target time period according to the first environmental data, the second environmental data, and an environmental prediction model; then, determine a target brightness in the target time period according to the third environmental data; and finally, adjust the brightness of the lighting unit on the current light pole to the target brightness within the target time period. By changing the original centralized control to the adaptive control of each street lamp and combining the data of adjacent light poles to calculate the required brightness, it is possible to avoid excessive or too low local brightness, thereby achieving better distributed intelligent control and accurately adjusting the brightness of each light pole.

Description

Technical Field

[0001] The present invention belongs to the technical field of lighting control, and in particular relates to an intelligent lighting system and a control method. Background Art

[0002] In the context of rapid development of modern urbanization, urban street lighting systems, as key infrastructure, not only play a vital role in ensuring night travel safety, improving the lighting effect of urban nightscapes, and saving energy consumption, but also face the challenge that traditional time control or light control methods cannot adapt to different weather, traffic flow and changes in citizen activity patterns, making it difficult to optimize energy utilization and lighting quality. The rise of Internet of Things technology has brought revolutionary changes to the street lighting system. Through sensors and communication technology to collect environmental data in real time, smart street lights can intelligently adjust brightness, color temperature and lighting duration to achieve energy saving and consumption reduction and provide a safer lighting environment, promoting the intelligence and automation of lighting control and improving the level of urban management.

[0003] The current smart street light system mainly relies on a central control system. This approach has limitations in processing large amounts of sensor data and meeting the diverse lighting needs of cities. It is highly dependent on the computing and decision-making capabilities of the central control system, and street lights are usually adjusted uniformly by region, and it is impossible to adaptively adjust each street light. Summary of the invention

[0004] In view of this, the present invention provides a smart lighting system and control method, which solves the problem that the existing technology relies on the calculation and decision-making capabilities of the central control system, usually uniformly adjusts street lights by area, and cannot adaptively adjust each street light.

[0005] A first aspect of an embodiment of the present invention provides a smart lighting system, the smart lighting system comprising a plurality of smart lamp poles, each of which is provided with a lighting unit, a processing unit, a communication unit and at least one environmental monitoring sensor; the processing unit is connected to the lighting unit and the communication unit respectively; the processing unit is connected to the at least one environmental monitoring sensor;

[0006] The processing unit is used to perform the following steps:

[0007] Acquire first environmental data monitored by an environmental monitoring sensor of the lamp pole and second environmental data monitored by an environmental monitoring sensor of an adjacent lamp pole of the lamp pole;

[0008] Predicting the third environmental data of the lamp pole in the target period according to the first environmental data, the second environmental data and the environmental prediction model;

[0009] Determining a target brightness during a target period according to the third environment data;

[0010] During the target time period, adjust the brightness of the lighting unit on this lamp post to the target brightness.

[0011] In a possible implementation, the processing unit is used to:

[0012] Predict the third environmental data of this lamp post during the target time period according to the first environmental data, the second environmental data, and the long short-term memory network model.

[0013] In a possible implementation, the first environmental data includes the first brightness, the first traffic flow within the lighting range of the current lamp post, and the second brightness within the lighting range of the adjacent lamp posts detected by the current lamp post; the second environmental data includes the third brightness and the second traffic flow within the lighting range of the adjacent lamp posts of the current lamp post; the processing unit is used to:

[0014] Predict the third traffic flow of this lamp post during the target time period according to the first traffic flow, the second traffic flow, and the long short-term memory network model;

[0015] Calculate the current visibility according to the first brightness, the second brightness, and the third brightness;

[0016] Take the third traffic flow and the current visibility as the third environmental data.

[0017] In a possible implementation, the processing unit is used to:

[0018] Calculate the minimum brightness of the target time period according to the third traffic flow and the current visibility;

[0019] Determine the target brightness according to the minimum brightness.

[0020] In a possible implementation, the first environmental data includes the first image, the first traffic flow within the lighting range of the current lamp post, and the second image within the lighting range of the adjacent lamp posts detected by the current lamp post; the second environmental data includes the third image and the second traffic flow within the lighting range of the adjacent lamp posts of the current lamp post; the processing unit is used to:

[0021] Predict the third traffic flow of this lamp post during the target time period according to the first traffic flow, the second traffic flow, and the long short-term memory network model;

[0022] Calculate the current visual comfort according to the first image, the second image, and the third image;

[0023] Take the third traffic flow and the current visual comfort as the third environmental data.

[0024] In a possible implementation, the processing unit is used to:

[0025] Calculate the comfortable brightness of the target time period according to the third traffic flow and the current visual comfort;

[0026] Determine the target brightness according to the comfortable brightness.

[0027] In a possible implementation, the processing unit is further configured to:

[0028] When the current moment is in the energy-saving period, use the minimum brightness as the target brightness; when the current moment is not in the energy-saving period, use the comfortable brightness as the target brightness.

[0029] In a possible implementation, the processing unit is further configured to:

[0030] When the current moment is in the energy-saving period, query the brightness gradient corresponding to the minimum brightness, and use the brightness value corresponding to the brightness gradient as the target brightness; when the current moment is not in the energy-saving period, use the brightness gradient corresponding to the comfortable brightness, and use the brightness value corresponding to the brightness gradient as the target brightness.

[0031] In a possible implementation, the processing unit is configured to:

[0032] When the difference between the current brightness and the target brightness is greater than the preset threshold, adjust the brightness of the lighting unit on this lamp post to the target brightness;

[0033] When the difference between the current brightness and the target brightness is not greater than the preset threshold, control the brightness of the lighting unit on this lamp post to remain unchanged.

[0034] The second aspect of the embodiments of the present invention provides a method for controlling a smart lighting system, including:

[0035] Obtain the first environmental data monitored by the environmental monitoring sensor of this lamp post and the second environmental data monitored by the environmental monitoring sensor of the adjacent lamp post of this lamp post;

[0036] Predict the third environmental data of this lamp post in the target period according to the first environmental data, the second environmental data, and the environmental prediction model;

[0037] Determine the target brightness of the target period according to the third environmental data;

[0038] During the target period, adjust the brightness of the lighting unit on this lamp post to the target brightness.

[0039] The intelligent lighting system and control method provided by the embodiments of the present invention, wherein the intelligent lighting system includes a plurality of intelligent light poles, and each intelligent light pole is provided with a lighting unit, a processing unit, a communication unit, and at least one environmental monitoring sensor; the processing unit is respectively connected to the lighting unit and the communication unit; the processing unit is connected to at least one environmental monitoring sensor; the processing unit is configured to perform the following steps: first, obtain first environmental data monitored by the environmental monitoring sensor of this light pole and second environmental data monitored by the environmental monitoring sensor of the adjacent light pole of this light pole; secondly, predict third environmental data of this light pole at the target time period according to the first environmental data, the second environmental data, and the environmental prediction model; then determine the target brightness at the target time period according to the third environmental data; finally, adjust the brightness of the lighting unit on this light pole to the target brightness within the target time period. By changing the original centralized control to the adaptive control of each street lamp, and at the same time combining the data of adjacent light poles to calculate the required brightness, it is avoided that the local brightness is too high or too low, so as to achieve better distributed intelligent control and accurately adjust the brightness of each light pole. Brief Description of the Drawings

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0041] Figure 1 is a schematic structural diagram of the intelligent lighting system provided by the embodiments of the present invention;

[0042] Figure 2 is a flowchart of the implementation of the control method of the intelligent lighting system provided by the embodiments of the present invention. Detailed Embodiments

[0043] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present invention. However, those skilled in the art should clearly understand that the present invention can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present invention.

[0044] Figure 1 is a schematic structural diagram of the intelligent lighting system provided by the embodiments of the present invention. As Figure 1As shown, in some embodiments, the intelligent lighting system includes: a plurality of intelligent light poles, each of which is provided with a lighting unit 11, a processing unit 12, a communication unit 13, and at least one environmental monitoring sensor 14; the processing unit 12 is respectively connected to the lighting unit 11 and the communication unit 13; the processing unit 12 is connected to at least one environmental monitoring sensor 14;

[0045] The processing unit 12 is configured to perform the following steps: obtain first environmental data monitored by the environmental monitoring sensor of this light pole and second environmental data monitored by the environmental monitoring sensors of the adjacent light poles of this light pole; predict third environmental data of this light pole at a target time period according to the first environmental data, the second environmental data, and an environmental prediction model; determine a target brightness at the target time period according to the third environmental data; and adjust the brightness of the lighting unit on this light pole to the target brightness within the target time period.

[0046] In the embodiments of the present invention, the lighting unit 11 may be an LED lamp, a solar street lamp, etc., which is not limited herein. The processing unit 12 may be a single-chip microcomputer, an MCU, etc., which is not limited herein. The communication unit 13 may be a wireless communication unit such as Bluetooth, or may be a wired communication unit. The environmental monitoring sensor 14 may be a light sensor and an infrared detection sensor for traffic flow detection, or may be a shooting unit for shooting environmental images. The target time period is the time period after the current moment. The adjacent light poles may be the two nearest light poles on the same side of the road as this light pole, or may be the light poles on the opposite side of the road of this light pole. In the case of an intersection, they may also be the light poles on the diagonal side, and specifically may be selected according to the actual situation. This is not limited herein. When there are multiple adjacent light poles, the average value of each adjacent light pole is taken as the calculated value in the following calculations.

[0047] In some embodiments, the processing unit is configured to: predict third environmental data of this light pole at a target time period according to the first environmental data, the second environmental data, and a long short-term memory network model.

[0048] In some embodiments, the first environmental data includes a first brightness, a first traffic flow within the lighting range of the current light pole, and a second brightness within the lighting range of the adjacent light poles detected by the current light pole; the second environmental data includes a third brightness and a second traffic flow within the lighting range of the adjacent light poles of the current light pole;

[0049] The processing unit is configured to: predict a third traffic flow of this light pole at a target time period according to the first traffic flow, the second traffic flow, and a long short-term memory network model; calculate the current visibility according to the first brightness, the second brightness, and the third brightness; and use the third traffic flow and the current visibility as the third environmental data.

[0050] In the embodiments of the present invention, the status and environment of street lights are dynamically changing. To achieve early regulation and contingency plan formulation, it is necessary to predict the changes in the status and environment of street lights in a future period of time. This can make the control of street lights more forward-looking and adaptable, and improve the intelligent level of management. The Long Short-Term Memory (LSTM) is a special type of recurrent neural network. LSTM maintains and transmits gradients through cell states and gating mechanisms, thus maintaining a stable gradient flow in long sequences and being able to well complete time series prediction. Specifically, historical traffic flow data is required, usually recorded at time intervals (such as every hour, every 15 minutes). It may also be necessary to collect other external factors affecting traffic flow, such as weather, holidays, nearby events, etc. A training set and a test set are formed to train the LSTM. In addition, data preprocessing is also required to handle missing values and outliers.

[0051] In the embodiments of the present invention, after obtaining the predicted traffic flow, the illumination of the street lights can be controlled on demand according to the traffic flow, so that the street lights are adaptively adjusted according to the traffic flow. In addition, other factors affecting street light illumination should also be considered. The traditional idea is to add more sensors related to environmental detection or visibility detection on the street light poles, but this method will significantly increase the complexity and cost of the street lights. Therefore, in the present invention, through the communication between two adjacent street light poles, image analysis is performed according to the brightness within the illumination range of the street lights, so as to calculate the visibility.

[0052] Specifically, the brightness difference between the second brightness and the third brightness can be calculated, and then the first brightness and this brightness difference are input into the trained support vector machine to obtain the current visibility. Among them, the brightness difference between the second brightness and the third brightness represents the difference between the illumination of the adjacent street lights observed by the current street light and the self-observation of the adjacent street lights. The larger this difference, the worse the visibility. At the same time, the calculation of visibility is affected by the brightness of the street light itself. Under the same visibility, the higher the brightness, the smaller the observed difference.

[0053] In some embodiments, the processing unit is configured to: calculate the minimum brightness of the target period according to the third traffic flow and the current visibility; determine the target brightness according to the minimum brightness.

[0054] In the embodiments of the present invention, in some embodiments, the first environmental data includes the first image within the illumination range of the current lamp post, the first traffic flow, and the second image within the illumination range of the adjacent lamp post detected by the current lamp post; the second environmental data includes the third image within the illumination range of the adjacent lamp post of the current lamp post and the second traffic flow;

[0055] The processing unit is used to: predict the third traffic flow of this lamp post in the target period according to the first traffic flow, the second traffic flow, and the long short-term memory network model; calculate the current visual comfort according to the first image, the second image, and the third image; and use the third traffic flow and the current visual comfort as the third environmental data.

[0056] Although the above method of considering traffic flow and visibility can achieve the intelligent control of the lamp post to a certain extent, the control effect may not be optimal. In the embodiments of the present invention, the concept of visual comfort is introduced to control the lamp post. Visual comfort analysis is a relatively subjective process, but it can be quantified through some objective indicators. For example, the brightness uniformity, contrast, resolution, sharpness, etc. of the image can be analyzed, and then combined with the above-mentioned visibility. Multiply each indicator by the corresponding preset weight to obtain the visual comfort.

[0057] Both too bright and too dark street lights will affect the change of uniformity in the image. When the overall brightness of the image increases, if the brightness of the bright part and the dark part both increase at the same time, the contrast of the image may decrease. This is because the difference between the bright part and the dark part becomes smaller. Resolution refers to the fineness of details in the image, usually determined by the number of pixels in the image. Brightness adjustment does not change the actual resolution of the image, that is, it does not increase or decrease the number of pixels in the image. However, brightness adjustment may affect the visibility of image details. For example, if the image is too bright or too dark, some details may become less obvious due to insufficient contrast, giving the illusion of a decrease in resolution. Sharpness involves the clarity of edges and details in the image. Brightness adjustment does not directly change the sharpness of the image, that is, it does not affect the clarity of edges in the image. However, by adjusting the brightness, the contrast of the image can be enhanced or weakened, thus affecting the perception of sharpness. For example, increasing the brightness may reduce the contrast of the image, making the image look softer, while decreasing the brightness may increase the contrast, making the image look sharper.

[0058] In the embodiments of the present invention, the brightness uniformity, contrast, resolution, and sharpness in the first image are extracted, and at the same time, brightness analysis is performed according to the first image, the second image, and the third image. Calculate the contrast in the manner of the above embodiment to obtain the index values of each index, and multiply them by the corresponding preset weights to obtain the visual comfort.

[0059] In some embodiments, the processing unit is used to:

[0060] Calculate the comfortable brightness in the target period according to the third traffic flow and the current visual comfort;

[0061] Determine the target brightness according to the comfortable brightness.

[0062] In the embodiments of the present invention, there is a standard visual comfort level under each traffic flow. If the current visual comfort level is less than the standard value, a smooth adjustment curve is fitted based on the current visual comfort level and the standard value, which is used as the comfortable brightness during the target period, so that the brightness of the street lamp is smoothly adjusted to the most comfortable brightness.

[0063] In some embodiments, the processing unit is further configured to: when the current moment is in the energy-saving period, use the minimum brightness as the target brightness; when the current moment is not in the energy-saving period, use the comfortable brightness as the target brightness.

[0064] In the embodiments of the present invention, different controls can also be performed according to time periods. For example, during the peak electricity consumption period, the minimum required brightness can be calculated according to visibility and traffic flow for the most energy-saving control, and during the non-peak electricity consumption period, optimal lighting control can be performed according to visual comfort.

[0065] In some embodiments, the processing unit is further configured to: when the current moment is in the energy-saving period, query the brightness gradient corresponding to the minimum brightness and use the brightness value corresponding to the brightness gradient as the target brightness; when the current moment is not in the energy-saving period, use the brightness gradient corresponding to the comfortable brightness and use the brightness value corresponding to the brightness gradient as the target brightness.

[0066] In the embodiments of the present invention, since the traffic flow is constantly changing, to avoid frequent fluctuations in brightness, causing visual discomfort and loss of the service life of the street lamp, multiple different brightness gradients can be set from small to large. After calculating the brightness, the gradient value closest to it is used as the actually used brightness value. In this way, when adjusting, the street lamp will only adjust between several brightness gradients, and the situation of frequent brightness fluctuations will not occur. In addition, fluctuations can also be avoided by setting a single threshold.

[0067] In some embodiments, the processing unit is configured to: when the difference between the current brightness and the target brightness is greater than the preset threshold, adjust the brightness of the lighting unit on this lamp post to the target brightness; when the difference between the current brightness and the target brightness is not greater than the preset threshold, control the brightness of the lighting unit on this lamp post to remain unchanged.

[0068] Figure 2 is the implementation flowchart of the intelligent lighting system control method provided by the embodiments of the present invention. As Figure 2 shown, in some embodiments, the intelligent lighting system control method includes:

[0069] S210, obtaining first environmental data monitored by the environmental monitoring sensor of this lamp post and second environmental data monitored by the environmental monitoring sensor of the adjacent lamp post of this lamp post;

[0070] S220. Predict the third environmental data of this lamp post during the target period according to the first environmental data, the second environmental data, and the environmental prediction model;

[0071] S230. Determine the target brightness during the target period according to the third environmental data;

[0072] S240. During the target period, adjust the brightness of the lighting unit on this lamp post to the target brightness.

[0073] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0074] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0075] In the embodiments provided by the present invention, it should be understood that the disclosed device / controller and method can be implemented in other ways. For example, the device / controller embodiments described above are merely illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical or other forms.

[0076] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0077] In addition, the functional units in each embodiment of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0078] When the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0079] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A smart lighting system, characterized in that, The intelligent lighting system includes a plurality of intelligent light poles, and each intelligent light pole is provided with a lighting unit, a processing unit, a communication unit, and at least one environmental monitoring sensor; the processing unit is respectively connected to the lighting unit and the communication unit; the processing unit is connected to the at least one environmental monitoring sensor; The processing unit is configured to perform the following steps: Obtain first environmental data monitored by the environmental monitoring sensor of this light pole and second environmental data monitored by the environmental monitoring sensor of the adjacent light pole of this light pole; Predict third environmental data of this light pole in the target time period according to the first environmental data, the second environmental data, and the environmental prediction model; Determine the target brightness in the target time period according to the third environmental data; During the target time period, adjust the brightness of the lighting unit on this light pole to the target brightness; The first environmental data includes the first brightness, the first traffic flow in the lighting range of the current light pole, and the second brightness in the lighting range of the adjacent light pole detected by the current light pole; the second environmental data includes the third brightness and the second traffic flow in the lighting range of the adjacent light pole of the current light pole; the processing unit is configured to: Predict the third traffic flow of this light pole in the target time period according to the first traffic flow, the second traffic flow, and the long short-term memory network model; Calculate the brightness difference between the second brightness and the third brightness, and input the first brightness and this brightness difference into the trained support vector machine to obtain the current visibility; Use the third traffic flow and the current visibility as the third environmental data; Alternatively, the first environmental data includes the first image, the first traffic flow in the lighting range of the current light pole, and the second image in the lighting range of the adjacent light pole detected by the current light pole; the second environmental data includes the third image and the second traffic flow in the lighting range of the adjacent light pole of the current light pole; the processing unit is configured to: Predict the third traffic flow of this light pole in the target time period according to the first traffic flow, the second traffic flow, and the long short-term memory network model; Extract the brightness uniformity, contrast, resolution, and sharpness in the first image, and at the same time perform brightness analysis according to the first image, the second image, and the third image, calculate the visibility, and obtain the index values of each index; multiply the index values of each index by the corresponding preset weights to obtain the current visual comfort level; Use the third traffic flow and the current visual comfort level as the third environmental data.

2. The intelligent lighting system according to claim 1, wherein The processing unit is configured to: Calculate the minimum brightness in the target time period according to the third traffic flow and the current visibility; Determine the target brightness according to the minimum brightness.

3. The intelligent lighting system according to claim 1, characterized in that, The processing unit is configured to: Calculate the comfortable brightness in the target time period according to the third traffic flow and the current visual comfort level; Determine the target brightness according to the comfortable brightness.

4. The intelligent lighting system according to claim 1, wherein The processing unit is configured to: When the difference between the current brightness and the target brightness is greater than the preset threshold, adjust the brightness of the lighting unit on this light pole to the target brightness; When the difference between the current brightness and the target brightness is not greater than the preset threshold, control the brightness of the lighting unit on this light pole to remain unchanged.

5. A control method for an intelligent lighting system, characterized in that Applied to the intelligent lighting system according to any one of claims 1-4 above; the method includes: Obtain the first environmental data monitored by the environmental monitoring sensor of this lamp post and the second environmental data monitored by the environmental monitoring sensors of the adjacent lamp posts of this lamp post; Predict the third environmental data of this lamp post during the target time period according to the first environmental data, the second environmental data and the environmental prediction model; Determine the target brightness during the target time period according to the third environmental data; During the target time period, adjust the brightness of the lighting unit on this lamp post to the target brightness.

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